Real-time posture feedback for effective motor learning in table tennis in virtual reality

Abstract

Table tennis is a demanding sport in terms of motor skills, requiring precise time-critical postures and techniques for the player to excel. Therefore, it is a compelling case study for motor learning in Virtual Reality (VR). Though VR table tennis training applications have been used to study the learning process in VR, each has not fully explored the role of real-time feedback on motor learning, which is a fundamental component of real-life table tennis training. To address this, we have developed a training system that provides multi-modal training instructions and real-time posture guidance feedback to assist beginner players in improving their playing outcomes. This system synthesizes a table tennis training environment by providing realistic visual and audio stimuli that includes a custom highly-accurate physics implementation of ball flight. An experimental group (n = 9) trained to learn correct posture and paddle handling for forehand and backhand drives. The results show that the participants improved significantly in terms of technique and ball return quality. This work is the first VR table tennis system that provides real-time posture feedback using a low-cost depth camera. Additionally, the participants’ significant improvement in playing posture over a short period of time shows the integral role of feedback in learning and further validates VR as an effective motor learning tool.